Overview

Brought to you by YData

Dataset statistics

Number of variables14
Number of observations8693
Missing cells2324
Missing cells (%)1.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory891.5 KiB
Average record size in memory105.0 B

Variable types

Text3
Categorical2
Boolean3
Numeric6

Alerts

FoodCourt is highly overall correlated with VRDeckHigh correlation
VRDeck is highly overall correlated with FoodCourtHigh correlation
VIP is highly imbalanced (84.0%) Imbalance
HomePlanet has 201 (2.3%) missing values Missing
CryoSleep has 217 (2.5%) missing values Missing
Cabin has 199 (2.3%) missing values Missing
Destination has 182 (2.1%) missing values Missing
Age has 179 (2.1%) missing values Missing
VIP has 203 (2.3%) missing values Missing
RoomService has 181 (2.1%) missing values Missing
FoodCourt has 183 (2.1%) missing values Missing
ShoppingMall has 208 (2.4%) missing values Missing
Spa has 183 (2.1%) missing values Missing
VRDeck has 188 (2.2%) missing values Missing
Name has 200 (2.3%) missing values Missing
PassengerId has unique values Unique
Age has 178 (2.0%) zeros Zeros
RoomService has 5577 (64.2%) zeros Zeros
FoodCourt has 5456 (62.8%) zeros Zeros
ShoppingMall has 5587 (64.3%) zeros Zeros
Spa has 5324 (61.2%) zeros Zeros
VRDeck has 5495 (63.2%) zeros Zeros

Reproduction

Analysis started2025-01-22 12:08:19.342479
Analysis finished2025-01-22 12:08:30.223948
Duration10.88 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

PassengerId
Text

Unique 

Distinct8693
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size68.0 KiB
2025-01-22T17:38:30.640104image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters60851
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8693 ?
Unique (%)100.0%

Sample

1st row0001_01
2nd row0002_01
3rd row0003_01
4th row0003_02
5th row0004_01
ValueCountFrequency (%)
0001_01 1
 
< 0.1%
0031_01 1
 
< 0.1%
0003_01 1
 
< 0.1%
0003_02 1
 
< 0.1%
0004_01 1
 
< 0.1%
0005_01 1
 
< 0.1%
0006_01 1
 
< 0.1%
0006_02 1
 
< 0.1%
0007_01 1
 
< 0.1%
0008_01 1
 
< 0.1%
Other values (8683) 8683
99.9%
2025-01-22T17:38:31.407945image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 12459
20.5%
1 9827
16.1%
_ 8693
14.3%
2 5017
8.2%
3 4039
 
6.6%
4 3790
 
6.2%
6 3664
 
6.0%
5 3606
 
5.9%
8 3557
 
5.8%
7 3410
 
5.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 60851
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 12459
20.5%
1 9827
16.1%
_ 8693
14.3%
2 5017
8.2%
3 4039
 
6.6%
4 3790
 
6.2%
6 3664
 
6.0%
5 3606
 
5.9%
8 3557
 
5.8%
7 3410
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 60851
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 12459
20.5%
1 9827
16.1%
_ 8693
14.3%
2 5017
8.2%
3 4039
 
6.6%
4 3790
 
6.2%
6 3664
 
6.0%
5 3606
 
5.9%
8 3557
 
5.8%
7 3410
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 60851
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 12459
20.5%
1 9827
16.1%
_ 8693
14.3%
2 5017
8.2%
3 4039
 
6.6%
4 3790
 
6.2%
6 3664
 
6.0%
5 3606
 
5.9%
8 3557
 
5.8%
7 3410
 
5.6%

HomePlanet
Categorical

Missing 

Distinct3
Distinct (%)< 0.1%
Missing201
Missing (%)2.3%
Memory size68.0 KiB
Earth
4602 
Europa
2131 
Mars
1759 

Length

Max length6
Median length5
Mean length5.0438059
Min length4

Characters and Unicode

Total characters42832
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEuropa
2nd rowEarth
3rd rowEuropa
4th rowEuropa
5th rowEarth

Common Values

ValueCountFrequency (%)
Earth 4602
52.9%
Europa 2131
24.5%
Mars 1759
 
20.2%
(Missing) 201
 
2.3%

Length

2025-01-22T17:38:31.837143image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-22T17:38:32.073410image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
earth 4602
54.2%
europa 2131
25.1%
mars 1759
 
20.7%

Most occurring characters

ValueCountFrequency (%)
a 8492
19.8%
r 8492
19.8%
E 6733
15.7%
t 4602
10.7%
h 4602
10.7%
u 2131
 
5.0%
o 2131
 
5.0%
p 2131
 
5.0%
M 1759
 
4.1%
s 1759
 
4.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 42832
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 8492
19.8%
r 8492
19.8%
E 6733
15.7%
t 4602
10.7%
h 4602
10.7%
u 2131
 
5.0%
o 2131
 
5.0%
p 2131
 
5.0%
M 1759
 
4.1%
s 1759
 
4.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 42832
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 8492
19.8%
r 8492
19.8%
E 6733
15.7%
t 4602
10.7%
h 4602
10.7%
u 2131
 
5.0%
o 2131
 
5.0%
p 2131
 
5.0%
M 1759
 
4.1%
s 1759
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 42832
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 8492
19.8%
r 8492
19.8%
E 6733
15.7%
t 4602
10.7%
h 4602
10.7%
u 2131
 
5.0%
o 2131
 
5.0%
p 2131
 
5.0%
M 1759
 
4.1%
s 1759
 
4.1%

CryoSleep
Boolean

Missing 

Distinct2
Distinct (%)< 0.1%
Missing217
Missing (%)2.5%
Memory size68.0 KiB
False
5439 
True
3037 
(Missing)
 
217
ValueCountFrequency (%)
False 5439
62.6%
True 3037
34.9%
(Missing) 217
 
2.5%
2025-01-22T17:38:32.296897image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Cabin
Text

Missing 

Distinct6560
Distinct (%)77.2%
Missing199
Missing (%)2.3%
Memory size68.0 KiB
2025-01-22T17:38:32.742548image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length8
Median length7
Mean length7.0775842
Min length5

Characters and Unicode

Total characters60117
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5427 ?
Unique (%)63.9%

Sample

1st rowB/0/P
2nd rowF/0/S
3rd rowA/0/S
4th rowA/0/S
5th rowF/1/S
ValueCountFrequency (%)
g/734/s 8
 
0.1%
b/11/s 7
 
0.1%
c/137/s 7
 
0.1%
b/82/s 7
 
0.1%
g/1368/p 7
 
0.1%
g/1476/s 7
 
0.1%
f/1194/p 7
 
0.1%
e/13/s 7
 
0.1%
c/21/p 7
 
0.1%
f/1411/p 7
 
0.1%
Other values (6550) 8423
99.2%
2025-01-22T17:38:33.497140image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 16988
28.3%
1 5326
 
8.9%
S 4288
 
7.1%
P 4206
 
7.0%
2 3078
 
5.1%
F 2794
 
4.6%
3 2601
 
4.3%
G 2559
 
4.3%
4 2393
 
4.0%
5 2377
 
4.0%
Other values (11) 13507
22.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 60117
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
/ 16988
28.3%
1 5326
 
8.9%
S 4288
 
7.1%
P 4206
 
7.0%
2 3078
 
5.1%
F 2794
 
4.6%
3 2601
 
4.3%
G 2559
 
4.3%
4 2393
 
4.0%
5 2377
 
4.0%
Other values (11) 13507
22.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 60117
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
/ 16988
28.3%
1 5326
 
8.9%
S 4288
 
7.1%
P 4206
 
7.0%
2 3078
 
5.1%
F 2794
 
4.6%
3 2601
 
4.3%
G 2559
 
4.3%
4 2393
 
4.0%
5 2377
 
4.0%
Other values (11) 13507
22.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 60117
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
/ 16988
28.3%
1 5326
 
8.9%
S 4288
 
7.1%
P 4206
 
7.0%
2 3078
 
5.1%
F 2794
 
4.6%
3 2601
 
4.3%
G 2559
 
4.3%
4 2393
 
4.0%
5 2377
 
4.0%
Other values (11) 13507
22.5%

Destination
Categorical

Missing 

Distinct3
Distinct (%)< 0.1%
Missing182
Missing (%)2.1%
Memory size68.0 KiB
TRAPPIST-1e
5915 
55 Cancri e
1800 
PSO J318.5-22
796 

Length

Max length13
Median length11
Mean length11.187052
Min length11

Characters and Unicode

Total characters95213
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTRAPPIST-1e
2nd rowTRAPPIST-1e
3rd rowTRAPPIST-1e
4th rowTRAPPIST-1e
5th rowTRAPPIST-1e

Common Values

ValueCountFrequency (%)
TRAPPIST-1e 5915
68.0%
55 Cancri e 1800
 
20.7%
PSO J318.5-22 796
 
9.2%
(Missing) 182
 
2.1%

Length

2025-01-22T17:38:33.828717image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-22T17:38:34.083552image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
trappist-1e 5915
45.8%
55 1800
 
13.9%
cancri 1800
 
13.9%
e 1800
 
13.9%
pso 796
 
6.2%
j318.5-22 796
 
6.2%

Most occurring characters

ValueCountFrequency (%)
P 12626
13.3%
T 11830
12.4%
e 7715
 
8.1%
S 6711
 
7.0%
- 6711
 
7.0%
1 6711
 
7.0%
A 5915
 
6.2%
I 5915
 
6.2%
R 5915
 
6.2%
5 4396
 
4.6%
Other values (13) 20768
21.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 95213
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
P 12626
13.3%
T 11830
12.4%
e 7715
 
8.1%
S 6711
 
7.0%
- 6711
 
7.0%
1 6711
 
7.0%
A 5915
 
6.2%
I 5915
 
6.2%
R 5915
 
6.2%
5 4396
 
4.6%
Other values (13) 20768
21.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 95213
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
P 12626
13.3%
T 11830
12.4%
e 7715
 
8.1%
S 6711
 
7.0%
- 6711
 
7.0%
1 6711
 
7.0%
A 5915
 
6.2%
I 5915
 
6.2%
R 5915
 
6.2%
5 4396
 
4.6%
Other values (13) 20768
21.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 95213
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
P 12626
13.3%
T 11830
12.4%
e 7715
 
8.1%
S 6711
 
7.0%
- 6711
 
7.0%
1 6711
 
7.0%
A 5915
 
6.2%
I 5915
 
6.2%
R 5915
 
6.2%
5 4396
 
4.6%
Other values (13) 20768
21.8%

Age
Real number (ℝ)

Missing  Zeros 

Distinct80
Distinct (%)0.9%
Missing179
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean28.82793
Minimum0
Maximum79
Zeros178
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size68.0 KiB
2025-01-22T17:38:34.353066image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q119
median27
Q338
95-th percentile56
Maximum79
Range79
Interquartile range (IQR)19

Descriptive statistics

Standard deviation14.489021
Coefficient of variation (CV)0.50260359
Kurtosis0.10193292
Mean28.82793
Median Absolute Deviation (MAD)9
Skewness0.41909658
Sum245441
Variance209.93174
MonotonicityNot monotonic
2025-01-22T17:38:34.687649image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24 324
 
3.7%
18 320
 
3.7%
21 311
 
3.6%
19 293
 
3.4%
23 292
 
3.4%
22 291
 
3.3%
20 277
 
3.2%
26 268
 
3.1%
28 267
 
3.1%
27 259
 
3.0%
Other values (70) 5612
64.6%
ValueCountFrequency (%)
0 178
2.0%
1 67
 
0.8%
2 75
0.9%
3 75
0.9%
4 71
 
0.8%
5 33
 
0.4%
6 40
 
0.5%
7 52
 
0.6%
8 46
 
0.5%
9 42
 
0.5%
ValueCountFrequency (%)
79 3
 
< 0.1%
78 3
 
< 0.1%
77 2
 
< 0.1%
76 2
 
< 0.1%
75 4
< 0.1%
74 5
0.1%
73 7
0.1%
72 4
< 0.1%
71 7
0.1%
70 9
0.1%

VIP
Boolean

Imbalance  Missing 

Distinct2
Distinct (%)< 0.1%
Missing203
Missing (%)2.3%
Memory size68.0 KiB
False
8291 
True
 
199
(Missing)
 
203
ValueCountFrequency (%)
False 8291
95.4%
True 199
 
2.3%
(Missing) 203
 
2.3%
2025-01-22T17:38:34.957589image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

RoomService
Real number (ℝ)

Missing  Zeros 

Distinct1273
Distinct (%)15.0%
Missing181
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean224.68762
Minimum0
Maximum14327
Zeros5577
Zeros (%)64.2%
Negative0
Negative (%)0.0%
Memory size68.0 KiB
2025-01-22T17:38:35.219086image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q347
95-th percentile1274.25
Maximum14327
Range14327
Interquartile range (IQR)47

Descriptive statistics

Standard deviation666.71766
Coefficient of variation (CV)2.9673093
Kurtosis65.273802
Mean224.68762
Median Absolute Deviation (MAD)0
Skewness6.3330141
Sum1912541
Variance444512.44
MonotonicityNot monotonic
2025-01-22T17:38:35.532225image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5577
64.2%
1 117
 
1.3%
2 79
 
0.9%
3 61
 
0.7%
4 47
 
0.5%
5 28
 
0.3%
9 25
 
0.3%
8 24
 
0.3%
6 24
 
0.3%
14 21
 
0.2%
Other values (1263) 2509
28.9%
(Missing) 181
 
2.1%
ValueCountFrequency (%)
0 5577
64.2%
1 117
 
1.3%
2 79
 
0.9%
3 61
 
0.7%
4 47
 
0.5%
5 28
 
0.3%
6 24
 
0.3%
7 17
 
0.2%
8 24
 
0.3%
9 25
 
0.3%
ValueCountFrequency (%)
14327 1
< 0.1%
9920 1
< 0.1%
8586 1
< 0.1%
8243 1
< 0.1%
8209 1
< 0.1%
8168 1
< 0.1%
8151 1
< 0.1%
8142 1
< 0.1%
8030 1
< 0.1%
7406 1
< 0.1%

FoodCourt
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct1507
Distinct (%)17.7%
Missing183
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean458.0772
Minimum0
Maximum29813
Zeros5456
Zeros (%)62.8%
Negative0
Negative (%)0.0%
Memory size68.0 KiB
2025-01-22T17:38:35.846710image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q376
95-th percentile2748.5
Maximum29813
Range29813
Interquartile range (IQR)76

Descriptive statistics

Standard deviation1611.4892
Coefficient of variation (CV)3.5179425
Kurtosis73.30723
Mean458.0772
Median Absolute Deviation (MAD)0
Skewness7.1022279
Sum3898237
Variance2596897.6
MonotonicityNot monotonic
2025-01-22T17:38:36.154970image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5456
62.8%
1 116
 
1.3%
2 75
 
0.9%
3 53
 
0.6%
4 53
 
0.6%
5 33
 
0.4%
6 31
 
0.4%
9 28
 
0.3%
7 27
 
0.3%
10 27
 
0.3%
Other values (1497) 2611
30.0%
(Missing) 183
 
2.1%
ValueCountFrequency (%)
0 5456
62.8%
1 116
 
1.3%
2 75
 
0.9%
3 53
 
0.6%
4 53
 
0.6%
5 33
 
0.4%
6 31
 
0.4%
7 27
 
0.3%
8 20
 
0.2%
9 28
 
0.3%
ValueCountFrequency (%)
29813 1
< 0.1%
27723 1
< 0.1%
27071 1
< 0.1%
26830 1
< 0.1%
21066 1
< 0.1%
18481 1
< 0.1%
17958 1
< 0.1%
17901 1
< 0.1%
17687 1
< 0.1%
17432 1
< 0.1%

ShoppingMall
Real number (ℝ)

Missing  Zeros 

Distinct1115
Distinct (%)13.1%
Missing208
Missing (%)2.4%
Infinite0
Infinite (%)0.0%
Mean173.72917
Minimum0
Maximum23492
Zeros5587
Zeros (%)64.3%
Negative0
Negative (%)0.0%
Memory size68.0 KiB
2025-01-22T17:38:36.455658image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q327
95-th percentile927.8
Maximum23492
Range23492
Interquartile range (IQR)27

Descriptive statistics

Standard deviation604.69646
Coefficient of variation (CV)3.4806847
Kurtosis328.87091
Mean173.72917
Median Absolute Deviation (MAD)0
Skewness12.627562
Sum1474092
Variance365657.81
MonotonicityNot monotonic
2025-01-22T17:38:36.754320image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5587
64.3%
1 153
 
1.8%
2 80
 
0.9%
3 59
 
0.7%
4 45
 
0.5%
5 38
 
0.4%
7 36
 
0.4%
6 34
 
0.4%
13 29
 
0.3%
8 28
 
0.3%
Other values (1105) 2396
27.6%
(Missing) 208
 
2.4%
ValueCountFrequency (%)
0 5587
64.3%
1 153
 
1.8%
2 80
 
0.9%
3 59
 
0.7%
4 45
 
0.5%
5 38
 
0.4%
6 34
 
0.4%
7 36
 
0.4%
8 28
 
0.3%
9 28
 
0.3%
ValueCountFrequency (%)
23492 1
< 0.1%
12253 1
< 0.1%
10705 1
< 0.1%
10424 1
< 0.1%
9058 1
< 0.1%
7810 1
< 0.1%
7185 1
< 0.1%
7148 1
< 0.1%
7104 1
< 0.1%
6805 1
< 0.1%

Spa
Real number (ℝ)

Missing  Zeros 

Distinct1327
Distinct (%)15.6%
Missing183
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean311.13878
Minimum0
Maximum22408
Zeros5324
Zeros (%)61.2%
Negative0
Negative (%)0.0%
Memory size68.0 KiB
2025-01-22T17:38:37.037923image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q359
95-th percentile1607.1
Maximum22408
Range22408
Interquartile range (IQR)59

Descriptive statistics

Standard deviation1136.7055
Coefficient of variation (CV)3.6533715
Kurtosis81.20211
Mean311.13878
Median Absolute Deviation (MAD)0
Skewness7.6360199
Sum2647791
Variance1292099.5
MonotonicityNot monotonic
2025-01-22T17:38:37.343948image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5324
61.2%
1 146
 
1.7%
2 105
 
1.2%
3 53
 
0.6%
5 53
 
0.6%
4 46
 
0.5%
7 34
 
0.4%
6 33
 
0.4%
9 28
 
0.3%
8 28
 
0.3%
Other values (1317) 2660
30.6%
(Missing) 183
 
2.1%
ValueCountFrequency (%)
0 5324
61.2%
1 146
 
1.7%
2 105
 
1.2%
3 53
 
0.6%
4 46
 
0.5%
5 53
 
0.6%
6 33
 
0.4%
7 34
 
0.4%
8 28
 
0.3%
9 28
 
0.3%
ValueCountFrequency (%)
22408 1
< 0.1%
18572 1
< 0.1%
16594 1
< 0.1%
16139 1
< 0.1%
15586 1
< 0.1%
15331 1
< 0.1%
15238 1
< 0.1%
14970 1
< 0.1%
13995 1
< 0.1%
13902 1
< 0.1%

VRDeck
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct1306
Distinct (%)15.4%
Missing188
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean304.85479
Minimum0
Maximum24133
Zeros5495
Zeros (%)63.2%
Negative0
Negative (%)0.0%
Memory size68.0 KiB
2025-01-22T17:38:37.969647image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q346
95-th percentile1534.2
Maximum24133
Range24133
Interquartile range (IQR)46

Descriptive statistics

Standard deviation1145.7172
Coefficient of variation (CV)3.7582391
Kurtosis86.011186
Mean304.85479
Median Absolute Deviation (MAD)0
Skewness7.8197316
Sum2592790
Variance1312667.9
MonotonicityNot monotonic
2025-01-22T17:38:38.292580image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5495
63.2%
1 139
 
1.6%
2 70
 
0.8%
3 56
 
0.6%
5 51
 
0.6%
4 47
 
0.5%
6 32
 
0.4%
8 30
 
0.3%
7 29
 
0.3%
9 25
 
0.3%
Other values (1296) 2531
29.1%
(Missing) 188
 
2.2%
ValueCountFrequency (%)
0 5495
63.2%
1 139
 
1.6%
2 70
 
0.8%
3 56
 
0.6%
4 47
 
0.5%
5 51
 
0.6%
6 32
 
0.4%
7 29
 
0.3%
8 30
 
0.3%
9 25
 
0.3%
ValueCountFrequency (%)
24133 1
< 0.1%
20336 1
< 0.1%
17306 1
< 0.1%
17074 1
< 0.1%
16337 1
< 0.1%
14485 1
< 0.1%
12708 1
< 0.1%
12685 1
< 0.1%
12682 1
< 0.1%
12424 1
< 0.1%

Name
Text

Missing 

Distinct8473
Distinct (%)99.8%
Missing200
Missing (%)2.3%
Memory size68.0 KiB
2025-01-22T17:38:38.747331image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length18
Median length15
Mean length13.833628
Min length7

Characters and Unicode

Total characters117489
Distinct characters53
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8453 ?
Unique (%)99.5%

Sample

1st rowMaham Ofracculy
2nd rowJuanna Vines
3rd rowAltark Susent
4th rowSolam Susent
5th rowWilly Santantines
ValueCountFrequency (%)
willy 20
 
0.1%
casonston 18
 
0.1%
oneiles 16
 
0.1%
domington 15
 
0.1%
litthews 15
 
0.1%
browlerson 14
 
0.1%
garnes 14
 
0.1%
cartez 14
 
0.1%
fulloydez 14
 
0.1%
hinglendez 13
 
0.1%
Other values (4880) 16833
99.1%
2025-01-22T17:38:39.491977image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 12691
 
10.8%
a 10251
 
8.7%
n 9155
 
7.8%
8493
 
7.2%
r 7707
 
6.6%
o 6563
 
5.6%
i 6456
 
5.5%
l 6231
 
5.3%
s 5299
 
4.5%
t 4552
 
3.9%
Other values (43) 40091
34.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 117489
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 12691
 
10.8%
a 10251
 
8.7%
n 9155
 
7.8%
8493
 
7.2%
r 7707
 
6.6%
o 6563
 
5.6%
i 6456
 
5.5%
l 6231
 
5.3%
s 5299
 
4.5%
t 4552
 
3.9%
Other values (43) 40091
34.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 117489
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 12691
 
10.8%
a 10251
 
8.7%
n 9155
 
7.8%
8493
 
7.2%
r 7707
 
6.6%
o 6563
 
5.6%
i 6456
 
5.5%
l 6231
 
5.3%
s 5299
 
4.5%
t 4552
 
3.9%
Other values (43) 40091
34.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 117489
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 12691
 
10.8%
a 10251
 
8.7%
n 9155
 
7.8%
8493
 
7.2%
r 7707
 
6.6%
o 6563
 
5.6%
i 6456
 
5.5%
l 6231
 
5.3%
s 5299
 
4.5%
t 4552
 
3.9%
Other values (43) 40091
34.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.6 KiB
True
4378 
False
4315 
ValueCountFrequency (%)
True 4378
50.4%
False 4315
49.6%
2025-01-22T17:38:39.767464image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Interactions

2025-01-22T17:38:27.684177image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-01-22T17:38:20.774980image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-01-22T17:38:22.101099image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-01-22T17:38:23.912063image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-01-22T17:38:25.198172image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-01-22T17:38:26.426864image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-01-22T17:38:27.905160image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-01-22T17:38:20.998839image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-01-22T17:38:22.347478image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-01-22T17:38:24.178863image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-01-22T17:38:25.416253image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-01-22T17:38:26.657694image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-01-22T17:38:28.128862image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-01-22T17:38:21.226364image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-01-22T17:38:22.583109image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-01-22T17:38:24.387278image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-01-22T17:38:25.635597image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-01-22T17:38:26.866943image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-01-22T17:38:28.333001image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-01-22T17:38:21.452981image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-01-22T17:38:22.806605image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-01-22T17:38:24.610004image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-01-22T17:38:25.846267image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-01-22T17:38:27.098238image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-01-22T17:38:28.529404image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-01-22T17:38:21.658657image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-01-22T17:38:23.497810image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-01-22T17:38:24.806288image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-01-22T17:38:26.041002image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-01-22T17:38:27.282648image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-01-22T17:38:28.730866image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-01-22T17:38:21.894464image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-01-22T17:38:23.697717image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-01-22T17:38:24.999547image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-01-22T17:38:26.231578image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-01-22T17:38:27.455408image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2025-01-22T17:38:39.934455image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
AgeCryoSleepDestinationFoodCourtHomePlanetRoomServiceShoppingMallSpaTransportedVIPVRDeck
Age1.0000.1120.0410.2080.2010.1230.1030.1970.1340.1180.181
CryoSleep0.1121.0000.1190.1610.1180.1530.0700.1400.4680.0800.127
Destination0.0410.1191.0000.0920.2620.0350.0090.0680.1110.0430.062
FoodCourt0.2080.1610.0921.0000.2620.1850.1870.4860.0600.1330.511
HomePlanet0.2010.1180.2620.2621.0000.1500.0540.1900.1950.1770.197
RoomService0.1230.1530.0350.1850.1501.0000.4430.2490.1620.0540.182
ShoppingMall0.1030.0700.0090.1870.0540.4431.0000.2570.0390.0000.194
Spa0.1970.1400.0680.4860.1900.2490.2571.0000.1750.0440.448
Transported0.1340.4680.1110.0600.1950.1620.0390.1751.0000.0350.155
VIP0.1180.0800.0430.1330.1770.0540.0000.0440.0351.0000.120
VRDeck0.1810.1270.0620.5110.1970.1820.1940.4480.1550.1201.000

Missing values

2025-01-22T17:38:29.028786image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-01-22T17:38:29.494892image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-01-22T17:38:29.914807image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

PassengerIdHomePlanetCryoSleepCabinDestinationAgeVIPRoomServiceFoodCourtShoppingMallSpaVRDeckNameTransported
00001_01EuropaFalseB/0/PTRAPPIST-1e39.0False0.00.00.00.00.0Maham OfracculyFalse
10002_01EarthFalseF/0/STRAPPIST-1e24.0False109.09.025.0549.044.0Juanna VinesTrue
20003_01EuropaFalseA/0/STRAPPIST-1e58.0True43.03576.00.06715.049.0Altark SusentFalse
30003_02EuropaFalseA/0/STRAPPIST-1e33.0False0.01283.0371.03329.0193.0Solam SusentFalse
40004_01EarthFalseF/1/STRAPPIST-1e16.0False303.070.0151.0565.02.0Willy SantantinesTrue
50005_01EarthFalseF/0/PPSO J318.5-2244.0False0.0483.00.0291.00.0Sandie HinetthewsTrue
60006_01EarthFalseF/2/STRAPPIST-1e26.0False42.01539.03.00.00.0Billex JacostaffeyTrue
70006_02EarthTrueG/0/STRAPPIST-1e28.0False0.00.00.00.0NaNCandra JacostaffeyTrue
80007_01EarthFalseF/3/STRAPPIST-1e35.0False0.0785.017.0216.00.0Andona BestonTrue
90008_01EuropaTrueB/1/P55 Cancri e14.0False0.00.00.00.00.0Erraiam FlaticTrue
PassengerIdHomePlanetCryoSleepCabinDestinationAgeVIPRoomServiceFoodCourtShoppingMallSpaVRDeckNameTransported
86839272_02EarthFalseF/1894/PTRAPPIST-1e21.0False86.03.0149.0208.0329.0Gordo SimsonFalse
86849274_01NaNTrueG/1508/PTRAPPIST-1e23.0False0.00.00.00.00.0Chelsa BulliseyTrue
86859275_01EuropaFalseA/97/PTRAPPIST-1e0.0False0.00.00.00.00.0Polaton ConableTrue
86869275_02EuropaFalseA/97/PTRAPPIST-1e32.0False1.01146.00.050.034.0Diram ConableFalse
86879275_03EuropaNaNA/97/PTRAPPIST-1e30.0False0.03208.00.02.0330.0Atlasym ConableTrue
86889276_01EuropaFalseA/98/P55 Cancri e41.0True0.06819.00.01643.074.0Gravior NoxnutherFalse
86899278_01EarthTrueG/1499/SPSO J318.5-2218.0False0.00.00.00.00.0Kurta MondalleyFalse
86909279_01EarthFalseG/1500/STRAPPIST-1e26.0False0.00.01872.01.00.0Fayey ConnonTrue
86919280_01EuropaFalseE/608/S55 Cancri e32.0False0.01049.00.0353.03235.0Celeon HontichreFalse
86929280_02EuropaFalseE/608/STRAPPIST-1e44.0False126.04688.00.00.012.0Propsh HontichreTrue